Map 2
Yu Du
Introduction:
The Data is collected from the FEMA website.
Data Cleaning Steps
step one - choose the ‘Hurricane’ as the incident of type - the incident type will affect the types of assistance available
step two - choose the year from 2009 to 2020
#Read Data: The data is saved from the data cleaning process.rmd
## X disasterNumber year pwNumber dcc damageCategory projectSize county
## 1 1 1866 2009 1 F Public Utilities Large Mobile
## 2 2 1866 2009 2 F Public Utilities Large Mobile
## 3 3 1866 2009 3 F Public Utilities Large Mobile
## 4 4 1866 2009 4 F Public Utilities Small Mobile
## 5 5 1866 2009 5 B Protective Measures Small Mobile
## 6 6 1866 2009 6 B Protective Measures Small Baldwin
## countyCode state stateCode stateNumberCode projectAmount
## 1 97 Alabama AL 1 0.00
## 2 97 Alabama AL 1 58425.34
## 3 97 Alabama AL 1 0.00
## 4 97 Alabama AL 1 12778.47
## 5 97 Alabama AL 1 15290.26
## 6 3 Alabama AL 1 9820.02
## federalShareObligated totalObligated
## 1 0.00 0.00
## 2 43819.01 43819.01
## 3 0.00 0.00
## 4 9583.85 9583.85
## 5 11467.70 11467.70
## 6 7365.02 7365.02Goal
- Calculate the total of two outcomes (project Amount, federal share obligated) in each county from 2009 to 2020
- Look at if there is any trend
- Compare two maps
Adding fips to the data.
Calculating the sum of project Amount for each county in 2009-2020.
## # A tibble: 8 x 9
## # Groups: fips, state, county [1]
## fips state county year project long lat group order
## <int> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1001 alabama autauga 2017 7016. -86.5 32.3 1 1
## 2 1001 alabama autauga 2017 7016. -86.5 32.4 1 2
## 3 1001 alabama autauga 2017 7016. -86.5 32.4 1 3
## 4 1001 alabama autauga 2017 7016. -86.6 32.4 1 4
## 5 1001 alabama autauga 2017 7016. -86.6 32.4 1 5
## 6 1001 alabama autauga 2017 7016. -86.6 32.4 1 6
## 7 1001 alabama autauga 2017 7016. -86.6 32.4 1 7
## 8 1001 alabama autauga 2017 7016. -86.6 32.4 1 8Using ggplot to draw a map for the total project amount of each state.
Using ggplot to draw a map for the total federal share Obligrated of each state
Using ggplotly
Now,I am trying to use ggplotly to show a clearer map for the previous dataframes.
Results/Observations
- The southeast US suffers the incident ‘Hurricane’.
- Coastal counties tend to have higher project amount and federal share obligated from 2009 to 2020.
- In some counties, total project amount is higher than total federal share obligated from 2009 to 2020.
What can improve or add
- can set time intervals in 2009-2020 to look at the data more specifically.
- can look at other incidents to find if some might have the similar result.
References
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2020). dplyr: A Grammar of Data Manipulation. R package version 1.0.2. https://CRAN.R-project.org/package=dplyr
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
Stefan Milton Bache and Hadley Wickham (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5. https://CRAN.R-project.org/package=magrittr
Original S code by Richard A. Becker, Allan R. Wilks. R version by Ray Brownrigg. Enhancements by Thomas P Minka and Alex Deckmyn. (2018). maps: Draw Geographical Maps. R package version 3.3.0. https://CRAN.R-project.org/package=maps
C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.